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          <h1 class="post-title" itemprop="name headline">机器学习西瓜书 （一）基础知识</h1>
        

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        <h1 id="引言"><a href="#引言" class="headerlink" title="引言"></a>引言</h1><p>计算机系统中，经验以数据形式体现</p>
<p>机器学习任务：从数据中产生模型</p>
<p>形式化定义：假设假设用P来评估计算机程序在任务T上的性能，若通过利用经验E获得改善，则说关于T和P，对E进行了学习。</p>
<h1 id="绪论"><a href="#绪论" class="headerlink" title="绪论"></a>绪论</h1><h2 id="基本术语"><a href="#基本术语" class="headerlink" title="基本术语"></a>基本术语</h2><p><strong>属性&amp;记录：</strong>（属性=记录）</p>
<p><strong>数据集：</strong> 示例的集合</p>
<p><strong>维数：</strong> 属性数目</p>
<p><strong>训练集：</strong> 用于训练模型的样本</p>
<p><strong>测试集：</strong> 对训练模型进行预测的样本</p>
<p><strong>样本空间：</strong> M个属性，每个有N种值，则空间规模为M*N</p>
<p><strong>假设空间：</strong></p>
<p>归纳：从特殊到一半的泛化过程</p>
<p>搜索策略：自顶向下，自底向上，一般到特殊，特殊到一般。搜索过程可以不断删除与正例不一致的假设，和与反例一致的假设。</p>
<p><strong>版本空间：</strong> 学习过程基于样本进行训练，称之为版本空间。</p>
<h2 id="归纳偏好"><a href="#归纳偏好" class="headerlink" title="归纳偏好"></a>归纳偏好</h2><p>机器学习过程中，对某类型假设的偏好。</p>
<p><strong>奥卡姆剃刀：</strong> 若有多个假设与观察一致，选最简单的那个。</p>
<p><strong>没有免费的午餐定理（NFL）：</strong> 所有“问题”的出现机会相同，或所有问题同等重要时，误差与学习算法无关。</p>
<p>结论： 谈论算法优劣必须针对具体的学习问题，学习算法自身的归纳偏好与问题是否匹配，往往有决定性作用。</p>

      
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